Color transfer for enhanced white blood cell classification in deep learning

Background and Objective Variations in staining protocols for peripheral blood smears lead to inconsistent image coloration, posing significant challenges for automated analysis. This study evaluates the effectiveness of color transfer as a preprocessing step to harmonize blood smear image color spaces and improve white blood cell classification performance using a convolutional neural network (CNN). Method Nine color transfer algorithms were evaluated using a May-Grünwald-Giemsa-stained image as a reference. A two-stage evaluation was employed: first, assessment of the color transfer effectiveness; and second, measurement of the impact of preprocessed images on a CNN model across three backbones (ResNet-50, Inception-V3 and EfficientNet-B3). Results The findings demonstrate that improper color transfer algorithms can inherently degrade clinical feature integrity by introducing unnatural visual elements. Specifically, the color distribution transfer method causes a significant drop in classification accuracy to 72.13%. By employing Gradient-weighted Class Activation Mapping (Grad-CAM), we empirically reveal that this method generates artifacts that mislead the spatial attention of CNN away from true leukocyte structures. Conversely, adopting Monge-Kantorovich linear color mapping on ResNet-50 architecture effectively preserves semantic structures without introducing false artifacts, which facilitates more localized model attention. This method yields the highest performance, achieving an accuracy of 94.46%, specificity of 98.57%, sensitivity of 94.40% and F1-score of 94.40%, a 3.57% relative improvement over the baseline model. The results underscore the importance of selecting a robust color transfer technique for building reliable automated white blood cell classification systems that remain accurate despite variations in staining protocol.